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Critical Care

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Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining...

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.

Critical care (London, England)
BACKGROUND: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis,...

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

Journal of critical care
PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on...

Prediction on critically ill patients: The role of "big data".

Journal of critical care
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...

A machine learning approach for mortality prediction only using non-invasive parameters.

Medical & biological engineering & computing
At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipmen...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

PloS one
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and publi...

Exploration of critical care data by using unsupervised machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups...